INVESTIGATING HOW AI CAN SUPPORT SELF-DIRECTED LEARNING FOR STUDENT TEACHERS IN AFRICAN RURAL UNIVERSITIES-PROSPECTS, CHALLENGES AND FUTURE - Rachel Gugu Mkhasibe; Oluwatoyin Ayodele Ajani (International Journal of Innovative Technologies in Social Science, 2024)
This study investigates how artificial intelligence (AI) can enhance self-directed learning among student teachers in African rural universities. A scoping review methodology was employed, encompassing 214 articles accessed from Scopus and Google Scholar. From these, 78 peer-reviewed English-language articles were selected for thematic analysis. The review highlights both the prospects and challenges of integrating AI into self-directed learning within these specific educational contexts. AI technologies offer significant potential to personalise learning experiences, provide adaptive feedback, and support remote learning in resource-constrained environments. However, the study also uncovers notable challenges, including limited infrastructure, inadequate digital literacy, and resistance to technology adoption. The findings suggest that while AI can significantly benefit self-directed learning, especially in areas where traditional educational resources are scarce, successful implementation requires overcoming these barriers through targeted interventions and support. Future research should focus on developing scalable AI solutions tailored to the unique needs of rural universities and exploring strategies to address the digital divide. This research provides a foundational understanding of AI’s role in supporting self-directed learning and offers practical insights for policymakers, educators, and researchers.